March 21, 2016
可以執行程式碼
你之前的程式碼沒打完(不知道是哪裡沒打完的話,按Esc砍掉重練)
log(-1)
## Warning in log(-1): NaNs produced
## [1] NaN
mena(NA)
## Error in eval(expr, envir, enclos): could not find function "mena"
記得先把NBA14-15的資料讀進來
if (!require('SportsAnalytics')){
install.packages("SportsAnalytics")
library(SportsAnalytics)
}
NBA1415<-fetch_NBAPlayerStatistics("14-15")
for(i in 1:nrow(NBA1415)){
if(NBA1415[i,"GamesPlayed"]>70&NBA1415[i,"TotalPoints"]>1500){
print(NBA1415[i,c("Name","Team","Position")])
}
}
## Name Team Position ## 9 Lamarcu Aldridge POR PF ## Name Team Position ## 112 Stephen Curry GSW PG ## Name Team Position ## 142 Monta Ellis DAL SG ## Name Team Position ## 197 James Harden HOU SG ## Name Team Position ## 231 Kyrie Irving CLE PG ## Name Team Position ## 283 Damian Lillard POR PG ## Name Team Position ## 361 Chris Paul LAC PG ## Name Team Position ## 442 Klay Thompson GSW SG
subset(NBA1415,GamesPlayed>70&
TotalPoints>1500)[,c("Name","Team","Position")]
## Name Team Position ## 9 Lamarcu Aldridge POR PF ## 112 Stephen Curry GSW PG ## 142 Monta Ellis DAL SG ## 197 James Harden HOU SG ## 231 Kyrie Irving CLE PG ## 283 Damian Lillard POR PG ## 361 Chris Paul LAC PG ## 442 Klay Thompson GSW SG
Data Frame的subset [ ] 也可以一行搞定!
NBA1415[NBA1415$GamesPlayed>70&
NBA1415$TotalPoints>1500,
c("Name","Team","Position")]
## Name Team Position ## 9 Lamarcu Aldridge POR PF ## 112 Stephen Curry GSW PG ## 142 Monta Ellis DAL SG ## 197 James Harden HOU SG ## 231 Kyrie Irving CLE PG ## 283 Damian Lillard POR PG ## 361 Chris Paul LAC PG ## 442 Klay Thompson GSW SG
想起上次上課…馬刺隊得分最高的好像是…
San<-subset(NBA1415,Team=='SAN') order(San$TotalPoints,decreasing = T)
## [1] 7 11 13 9 8 6 4 10 3 14 12 5 2 1 15
San[order(San$TotalPoints,decreasing = T)[1],
c("Name","Team","TotalPoints")]
## Name Team TotalPoints ## 137 Tim Duncan SAN 1070
如果要得到每隊最高分的球員名單呢?
NBA14-15球季,每隊最高分的球員名單
unique(NBA1415$Team)
## [1] NYK MEM OKL MIN POR NOR PHI IND DAL MIA BRO SAN MIL DET ATL HOU DEN ## [18] CHI GSW LAC PHO BOS WAS UTA SAC CHA LAL TOR CLE ORL ## 30 Levels: ATL BOS BRO CHA CHI CLE DAL DEN DET GSW HOU IND LAC LAL ... WAS
for(team in unique(NBA1415$Team)){
selectTeam<-subset(NBA1415,Team==team)
print(selectTeam[order(selectTeam$TotalPoints,decreasing = T)[1],
c("Name","Team","TotalPoints")])
}
## Name Team TotalPoints ## 19 Carmelo Anthony NYK 966 ## Name Team TotalPoints ## 166 Marc Gasol MEM 1413 ## Name Team TotalPoints ## 470 Russel Westbrook OKL 1886 ## Name Team TotalPoints ## 473 Andrew Wiggins MIN 1387 ## Name Team TotalPoints ## 283 Damian Lillard POR 1720 ## Name Team TotalPoints ## 117 Anthony Davis NOR 1656 ## Name Team TotalPoints ## 105 Robert Covington PHI 927 ## Name Team TotalPoints ## 317 C.j. Miles IND 942 ## Name Team TotalPoints ## 142 Monta Ellis DAL 1513 ## Name Team TotalPoints ## 458 Dwyane Wade MIA 1331 ## Name Team TotalPoints ## 286 Brook Lopez BRO 1236 ## Name Team TotalPoints ## 137 Tim Duncan SAN 1070 ## Name Team TotalPoints ## 316 Khris Middleton MIL 1055 ## Name Team TotalPoints ## 135 Andre Drummond DET 1130 ## Name Team TotalPoints ## 324 Paul Millsap ATL 1218 ## Name Team TotalPoints ## 197 James Harden HOU 2217 ## Name Team TotalPoints ## 274 Ty Lawson DEN 1143 ## Name Team TotalPoints ## 167 Pau Gasol CHI 1446 ## Name Team TotalPoints ## 112 Stephen Curry GSW 1900 ## Name Team TotalPoints ## 361 Chris Paul LAC 1564 ## Name Team TotalPoints ## 52 Eric Bledsoe PHO 1377 ## Name Team TotalPoints ## 436 Isaiah Thomas BOS 1101 ## Name Team TotalPoints ## 462 John Wall WAS 1385 ## Name Team TotalPoints ## 206 Gordon Hayward UTA 1463 ## Name Team TotalPoints ## 168 Rudy Gay SAC 1432 ## Name Team TotalPoints ## 236 Al Jefferson CHA 1080 ## Name Team TotalPoints ## 215 Jordan Hill LAL 841 ## Name Team TotalPoints ## 289 Kyle Lowry TOR 1244 ## Name Team TotalPoints ## 235 Lebron James CLE 1740 ## Name Team TotalPoints ## 457 Nikola Vucevic ORL 1428
FinalOutput<-NULL
for(team in unique(NBA1415$Team)){
selectTeam<-subset(NBA1415,Team==team)
FinalOutput<-rbind(FinalOutput,
selectTeam[
order(selectTeam$TotalPoints,decreasing = T)[1],
c("Name","Team","TotalPoints")])
}
FinalOutput
## Name Team TotalPoints ## 19 Carmelo Anthony NYK 966 ## 166 Marc Gasol MEM 1413 ## 470 Russel Westbrook OKL 1886 ## 473 Andrew Wiggins MIN 1387 ## 283 Damian Lillard POR 1720 ## 117 Anthony Davis NOR 1656 ## 105 Robert Covington PHI 927 ## 317 C.j. Miles IND 942 ## 142 Monta Ellis DAL 1513 ## 458 Dwyane Wade MIA 1331 ## 286 Brook Lopez BRO 1236 ## 137 Tim Duncan SAN 1070 ## 316 Khris Middleton MIL 1055 ## 135 Andre Drummond DET 1130 ## 324 Paul Millsap ATL 1218 ## 197 James Harden HOU 2217 ## 274 Ty Lawson DEN 1143 ## 167 Pau Gasol CHI 1446 ## 112 Stephen Curry GSW 1900 ## 361 Chris Paul LAC 1564 ## 52 Eric Bledsoe PHO 1377 ## 436 Isaiah Thomas BOS 1101 ## 462 John Wall WAS 1385 ## 206 Gordon Hayward UTA 1463 ## 168 Rudy Gay SAC 1432 ## 236 Al Jefferson CHA 1080 ## 215 Jordan Hill LAL 841 ## 289 Kyle Lowry TOR 1244 ## 235 Lebron James CLE 1740 ## 457 Nikola Vucevic ORL 1428
有類似for迴圈的功能
apply(NBA1415,2,max)
## League Name Team ## "NBA" "Zoran Dragic" "WAS" ## Position GamesPlayed TotalMinutesPlayed ## "SG" "83" "2979" ## FieldGoalsMade FieldGoalsAttempted ThreesMade ## "659" "1471" "286" ## ThreesAttempted FreeThrowsMade FreeThrowsAttempted ## "646" "715" "824" ## OffensiveRebounds TotalRebounds Assists ## "437" "1226" "838" ## Steals Turnovers Blocks ## "163" "321" "200" ## PersonalFouls Disqualifications TotalPoints ## "285" "10" "2217" ## Technicals Ejections FlagrantFouls ## "15" "1" "0" ## GamesStarted ## "82"
NBA1415球季,出場數、出場總分鐘數、總分的最大值各是多少
apply(NBA1415[,c("GamesPlayed","TotalMinutesPlayed","TotalPoints")]
,2,mean)
## GamesPlayed TotalMinutesPlayed TotalPoints ## 52.81707 1210.46341 499.95935
sapply(iris, mean)
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical: ## returning NA
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 5.843333 3.057333 3.758000 1.199333 NA
NBA1415球季,出場數、出場總分鐘數、總分的平均值各是多少
sapply(NBA1415[,c("GamesPlayed","TotalMinutesPlayed","TotalPoints")],
mean)
## GamesPlayed TotalMinutesPlayed TotalPoints ## 52.81707 1210.46341 499.95935
等同於
apply(NBA1415[,c("GamesPlayed","TotalMinutesPlayed","TotalPoints")]
,2,mean)
## GamesPlayed TotalMinutesPlayed TotalPoints ## 52.81707 1210.46341 499.95935
lapply(iris, mean)
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical: ## returning NA
## $Sepal.Length ## [1] 5.843333 ## ## $Sepal.Width ## [1] 3.057333 ## ## $Petal.Length ## [1] 3.758 ## ## $Petal.Width ## [1] 1.199333 ## ## $Species ## [1] NA
NBA1415球季,出場數、出場總分鐘數、總分的平均值各是多少
lapply(NBA1415[,c("GamesPlayed","TotalMinutesPlayed","TotalPoints")],
mean)
## $GamesPlayed ## [1] 52.81707 ## ## $TotalMinutesPlayed ## [1] 1210.463 ## ## $TotalPoints ## [1] 499.9593
向量分群後計算
tapply(NBA1415$Name,NBA1415$Team,length)
## ATL BOS BRO CHA CHI CLE DAL DEN DET GSW HOU IND LAC LAL MEM MIA MIL MIN ## 15 15 17 16 14 16 16 15 15 15 16 15 19 18 17 18 17 20 ## NOR NYK OKL ORL PHI PHO POR SAC SAN TOR UTA WAS ## 19 16 16 15 19 16 16 17 15 15 17 17
有tapply()的部分功能:替向量做分群
1到30的向量,用第二個參數做分群
split(1:30,gl(3, 10))
## $`1` ## [1] 1 2 3 4 5 6 7 8 9 10 ## ## $`2` ## [1] 11 12 13 14 15 16 17 18 19 20 ## ## $`3` ## [1] 21 22 23 24 25 26 27 28 29 30
輸出結果是List—>應該要想到lapply()
split()分群後的向量,用lapply()做運算
lapply(split(1:30,gl(3, 10)),mean)
## $`1` ## [1] 5.5 ## ## $`2` ## [1] 15.5 ## ## $`3` ## [1] 25.5
效果等同於
tapply(1:30,gl(3, 10),mean)
## 1 2 3 ## 5.5 15.5 25.5
替Data Frame做分群
NBA1415Team<-split(NBA1415[,c("TotalPoints","GamesPlayed")],
NBA1415$Team)
NBA1415Team
## $ATL ## TotalPoints GamesPlayed ## 21 356 63 ## 40 390 75 ## 61 96 36 ## 84 883 71 ## 121 129 35 ## 222 1156 76 ## 239 135 24 ## 266 911 75 ## 292 299 55 ## 324 1218 73 ## 338 197 40 ## 401 768 77 ## 403 529 68 ## 404 278 52 ## 431 1162 73 ## ## $BOS ## TotalPoints GamesPlayed ## 36 866 82 ## 60 1071 77 ## 108 628 82 ## 115 105 21 ## 241 448 75 ## 352 656 64 ## 372 177 50 ## 379 24 21 ## 412 523 67 ## 429 770 58 ## 436 1101 67 ## 449 779 82 ## 463 35 32 ## 488 105 31 ## 492 833 82 ## ## $BRO ## TotalPoints GamesPlayed ## 15 543 74 ## 54 700 78 ## 66 216 47 ## 93 20 9 ## 116 142 27 ## 232 957 80 ## 237 183 50 ## 246 1154 80 ## 255 138 44 ## 259 153 33 ## 263 3 7 ## 286 1236 72 ## 330 81 37 ## 367 717 82 ## 432 339 40 ## 475 886 68 ## 490 1069 76 ## ## $CHA ## TotalPoints GamesPlayed ## 48 304 64 ## 114 176 47 ## 192 254 45 ## 208 969 80 ## 236 1080 65 ## 261 598 55 ## 301 203 61 ## 356 41 9 ## 387 480 72 ## 423 501 61 ## 430 127 29 ## 456 83 25 ## 461 1075 62 ## 479 579 78 ## 480 964 68 ## 491 472 62 ## ## $CHI ## TotalPoints GamesPlayed ## 28 10 18 ## 63 954 82 ## 74 1301 65 ## 138 595 63 ## 167 1446 78 ## 171 642 62 ## 217 377 66 ## 308 109 36 ## 325 833 82 ## 326 25 23 ## 328 149 56 ## 343 485 67 ## 392 904 51 ## 419 435 72 ## ## $CLE ## TotalPoints GamesPlayed ## 91 15 8 ## 123 305 67 ## 201 136 50 ## 207 35 22 ## 231 1622 75 ## 235 1740 69 ## 251 244 57 ## 264 4 5 ## 288 1238 76 ## 295 278 58 ## 320 109 52 ## 335 792 81 ## 407 488 62 ## 415 844 70 ## 443 691 82 ## 453 255 26 ## ## $DAL ## TotalPoints GamesPlayed ## 12 412 74 ## 30 580 77 ## 89 771 75 ## 142 1513 80 ## 153 108 29 ## 199 665 76 ## 234 44 16 ## 238 432 74 ## 347 1333 77 ## 359 1037 66 ## 365 242 68 ## 371 90 29 ## 391 608 68 ## 413 80 42 ## 427 680 59 ## 455 403 64 ## ## $DEN ## TotalPoints GamesPlayed ## 23 382 58 ## 35 397 58 ## 90 1085 78 ## 94 57 30 ## 150 946 75 ## 156 435 51 ## 157 3 3 ## 162 734 59 ## 185 146 43 ## 200 188 55 ## 212 552 73 ## 272 93 24 ## 274 1143 75 ## 341 523 63 ## 348 426 62 ## ## $DET ## TotalPoints GamesPlayed ## 20 87 49 ## 73 460 78 ## 81 1043 82 ## 128 145 34 ## 135 1130 82 ## 233 1117 77 ## 240 632 41 ## 290 98 21 ## 297 36 23 ## 314 663 60 ## 321 29 10 ## 327 1098 69 ## 376 434 58 ## 445 482 76 ## 482 341 63 ## ## $GSW ## TotalPoints GamesPlayed ## 29 467 66 ## 32 827 82 ## 55 422 67 ## 112 1900 80 ## 149 201 46 ## 184 921 79 ## 219 254 59 ## 228 604 77 ## 268 20 16 ## 277 388 49 ## 285 461 78 ## 304 62 15 ## 396 30 33 ## 420 791 76 ## 442 1668 77 ## ## $HOU ## TotalPoints GamesPlayed ## 22 1048 82 ## 46 568 56 ## 62 917 80 ## 83 32 12 ## 129 184 69 ## 164 45 14 ## 197 2217 81 ## 223 646 41 ## 247 74 28 ## 253 386 33 ## 307 487 62 ## 334 855 71 ## 355 182 43 ## 375 274 67 ## 417 1027 83 ## 435 541 77 ## ## $IND ## TotalPoints GamesPlayed ## 10 317 63 ## 102 309 50 ## 170 53 6 ## 211 802 76 ## 214 692 43 ## 216 729 82 ## 293 263 61 ## 317 942 70 ## 395 323 68 ## 402 761 81 ## 411 393 53 ## 428 896 71 ## 465 570 57 ## 469 769 66 ## 472 57 20 ## ## $LAC ## TotalPoints GamesPlayed ## 33 764 76 ## 107 1010 64 ## 110 35 19 ## 119 294 74 ## 131 19 12 ## 151 167 36 ## 190 1469 67 ## 193 38 14 ## 204 425 73 ## 224 18 5 ## 250 21 33 ## 254 946 82 ## 361 1564 82 ## 381 1277 78 ## 385 530 76 ## 389 238 42 ## 448 232 62 ## 450 29 33 ## 474 41 21 ## ## $LAL ## TotalPoints GamesPlayed ## 49 379 63 ## 53 22 2 ## 58 836 71 ## 64 227 19 ## 68 782 35 ## 76 52 6 ## 95 703 59 ## 118 656 79 ## 141 650 65 ## 209 20 9 ## 215 841 70 ## 249 753 76 ## 260 332 52 ## 284 832 74 ## 374 221 43 ## 378 2 1 ## 397 307 67 ## 489 563 42 ## ## $MEM ## TotalPoints GamesPlayed ## 2 94 30 ## 11 539 63 ## 79 243 58 ## 85 384 66 ## 100 1107 70 ## 166 1413 81 ## 187 62 24 ## 188 1168 78 ## 267 419 81 ## 276 777 77 ## 282 286 63 ## 291 0 1 ## 380 1143 71 ## 418 30 12 ## 426 57 19 ## 439 4 2 ## 451 605 79 ## ## $MIA ## TotalPoints GamesPlayed ## 14 320 60 ## 42 212 24 ## 59 928 44 ## 67 20 5 ## 88 813 80 ## 120 3 4 ## 124 1007 72 ## 132 1275 78 ## 133 28 16 ## 143 312 62 ## 181 188 31 ## 203 261 62 ## 248 190 32 ## 313 72 17 ## 339 261 51 ## 458 1331 62 ## 460 175 24 ## 471 564 48 ## ## $MIL ## TotalPoints GamesPlayed ## 18 1030 81 ## 38 598 77 ## 86 966 67 ## 136 518 72 ## 144 121 33 ## 191 53 20 ## 210 471 67 ## 229 669 58 ## 296 118 28 ## 298 20 11 ## 302 806 71 ## 316 1055 79 ## 349 100 34 ## 354 606 73 ## 357 308 25 ## 368 294 73 ## 400 196 27 ## ## $MIN ## TotalPoints GamesPlayed ## 4 60 17 ## 45 298 57 ## 65 122 29 ## 69 457 67 ## 127 710 73 ## 165 323 47 ## 194 219 41 ## 225 198 45 ## 262 22 4 ## 273 778 77 ## 299 781 39 ## 336 512 38 ## 340 543 54 ## 353 27 6 ## 362 213 32 ## 364 386 31 ## 377 8 5 ## 394 226 22 ## 447 0 2 ## 473 1387 82 ## ## $NOR ## TotalPoints GamesPlayed ## 6 443 68 ## 17 833 61 ## 24 557 77 ## 27 256 63 ## 97 573 75 ## 109 340 66 ## 117 1656 68 ## 130 51 12 ## 147 1313 79 ## 159 178 50 ## 179 818 61 ## 218 592 40 ## 315 6 4 ## 319 2 5 ## 369 538 75 ## 398 42 21 ## 477 37 13 ## 483 98 37 ## 484 42 21 ## ## $NYK ## TotalPoints GamesPlayed ## 1 398 68 ## 8 338 61 ## 13 259 53 ## 19 966 40 ## 31 430 29 ## 80 382 42 ## 113 128 32 ## 140 210 39 ## 163 533 45 ## 196 804 70 ## 271 470 76 ## 275 90 17 ## 408 434 42 ## 416 656 82 ## 437 443 62 ## 467 199 51 ## ## $OKL ## TotalPoints GamesPlayed ## 3 537 70 ## 25 777 82 ## 99 273 66 ## 139 686 27 ## 227 917 64 ## 252 187 43 ## 258 1163 75 ## 269 295 47 ## 309 201 32 ## 333 790 74 ## 346 63 35 ## 386 228 68 ## 410 480 80 ## 433 131 16 ## 459 940 80 ## 470 1886 67 ## ## $ORL ## TotalPoints GamesPlayed ## 122 216 59 ## 155 698 58 ## 161 549 75 ## 176 243 47 ## 177 349 56 ## 189 306 52 ## 198 158 45 ## 202 1164 68 ## 294 37 16 ## 342 194 40 ## 350 294 51 ## 351 1292 72 ## 363 731 82 ## 384 188 47 ## 457 1428 74 ## ## $PHI ## TotalPoints GamesPlayed ## 7 93 41 ## 82 432 48 ## 105 927 69 ## 134 46 12 ## 178 17 9 ## 182 411 65 ## 278 0 1 ## 303 660 67 ## 310 106 23 ## 344 744 75 ## 383 172 19 ## 388 73 35 ## 390 308 54 ## 399 386 74 ## 409 586 73 ## 414 338 55 ## 438 45 17 ## 440 626 71 ## 487 507 30 ## ## $PHO ## TotalPoints GamesPlayed ## 34 32 16 ## 52 1377 81 ## 70 69 36 ## 111 0 2 ## 175 231 41 ## 186 884 74 ## 265 1070 63 ## 279 432 69 ## 312 9 6 ## 331 845 81 ## 332 1258 82 ## 373 131 25 ## 444 380 48 ## 446 713 78 ## 464 245 40 ## 485 544 75 ## ## $POR ## TotalPoints GamesPlayed ## 5 1035 78 ## 9 1661 71 ## 37 664 71 ## 51 350 81 ## 96 24 10 ## 106 168 51 ## 158 57 11 ## 160 169 48 ## 169 241 54 ## 257 638 74 ## 281 327 55 ## 283 1720 82 ## 287 566 59 ## 300 956 60 ## 306 424 62 ## 486 219 48 ## ## $SAC ## TotalPoints GamesPlayed ## 47 2 3 ## 87 593 67 ## 98 725 45 ## 104 1421 59 ## 146 176 47 ## 168 1432 68 ## 220 137 46 ## 270 503 70 ## 305 503 68 ## 311 996 82 ## 318 355 81 ## 329 2 3 ## 422 319 73 ## 425 8 3 ## 441 491 81 ## 466 0 2 ## 476 617 74 ## ## $SAN ## TotalPoints GamesPlayed ## 16 74 33 ## 26 137 51 ## 39 461 70 ## 43 568 62 ## 56 264 72 ## 126 708 81 ## 137 1070 77 ## 172 738 70 ## 183 946 81 ## 256 535 79 ## 280 1057 64 ## 322 351 51 ## 358 976 68 ## 421 428 52 ## 481 37 20 ## ## $TOR ## TotalPoints GamesPlayed ## 78 10 8 ## 125 1204 60 ## 154 46 26 ## 195 270 74 ## 205 50 29 ## 243 694 75 ## 245 554 70 ## 289 1244 70 ## 345 4 4 ## 360 648 81 ## 393 807 82 ## 424 14 17 ## 452 963 80 ## 454 775 82 ## 478 1242 80 ## ## $UTA ## TotalPoints GamesPlayed ## 44 0 2 ## 57 567 79 ## 71 973 76 ## 72 374 27 ## 92 6 4 ## 101 27 16 ## 103 80 16 ## 145 90 38 ## 148 393 82 ## 152 1187 74 ## 173 686 82 ## 206 1463 76 ## 221 433 50 ## 230 396 79 ## 242 16 8 ## 244 166 29 ## 323 248 47 ## ## $WAS ## TotalPoints GamesPlayed ## 41 964 63 ## 50 56 29 ## 75 580 75 ## 77 22 7 ## 174 277 51 ## 180 1001 82 ## 213 737 68 ## 226 509 64 ## 337 6 5 ## 366 868 73 ## 370 445 74 ## 382 11 5 ## 405 520 79 ## 406 402 64 ## 434 204 52 ## 462 1385 79 ## 468 106 32
lapply(NBA1415Team, colMeans) #回傳List
## $ATL ## TotalPoints GamesPlayed ## 567.13333 59.53333 ## ## $BOS ## TotalPoints GamesPlayed ## 541.4 59.4 ## ## $BRO ## TotalPoints GamesPlayed ## 502.17647 53.17647 ## ## $CHA ## TotalPoints GamesPlayed ## 494.1250 55.1875 ## ## $CHI ## TotalPoints GamesPlayed ## 590.35714 58.64286 ## ## $CLE ## TotalPoints GamesPlayed ## 549.75 53.75 ## ## $DAL ## TotalPoints GamesPlayed ## 562.375 60.875 ## ## $DEN ## TotalPoints GamesPlayed ## 474.0 53.8 ## ## $DET ## TotalPoints GamesPlayed ## 519.66667 54.86667 ## ## $GSW ## TotalPoints GamesPlayed ## 601.0667 60.0000 ## ## $HOU ## TotalPoints GamesPlayed ## 592.6875 56.1875 ## ## $IND ## TotalPoints GamesPlayed ## 525.0667 57.8000 ## ## $LAC ## TotalPoints GamesPlayed ## 479.84211 49.94737 ## ## $LAL ## TotalPoints GamesPlayed ## 454.33333 46.27778 ## ## $MEM ## TotalPoints GamesPlayed ## 490.05882 51.47059 ## ## $MIA ## TotalPoints GamesPlayed ## 442.22222 42.88889 ## ## $MIL ## TotalPoints GamesPlayed ## 466.41176 52.70588 ## ## $MIN ## TotalPoints GamesPlayed ## 363.5 38.4 ## ## $NOR ## TotalPoints GamesPlayed ## 440.78947 47.15789 ## ## $NYK ## TotalPoints GamesPlayed ## 421.2500 50.5625 ## ## $OKL ## TotalPoints GamesPlayed ## 597.125 57.875 ## ## $ORL ## TotalPoints GamesPlayed ## 523.13333 56.13333 ## ## $PHI ## TotalPoints GamesPlayed ## 340.89474 44.10526 ## ## $PHO ## TotalPoints GamesPlayed ## 513.7500 51.0625 ## ## $POR ## TotalPoints GamesPlayed ## 576.1875 57.1875 ## ## $SAC ## TotalPoints GamesPlayed ## 487.05882 51.29412 ## ## $SAN ## TotalPoints GamesPlayed ## 556.66667 62.06667 ## ## $TOR ## TotalPoints GamesPlayed ## 568.33333 55.86667 ## ## $UTA ## TotalPoints GamesPlayed ## 417.94118 46.17647 ## ## $WAS ## TotalPoints GamesPlayed ## 476.05882 53.05882
sapply(NBA1415Team, colMeans) #回傳Data frame
## ATL BOS BRO CHA CHI CLE DAL ## TotalPoints 567.13333 541.4 502.17647 494.1250 590.35714 549.75 562.375 ## GamesPlayed 59.53333 59.4 53.17647 55.1875 58.64286 53.75 60.875 ## DEN DET GSW HOU IND LAC LAL ## TotalPoints 474.0 519.66667 601.0667 592.6875 525.0667 479.84211 454.33333 ## GamesPlayed 53.8 54.86667 60.0000 56.1875 57.8000 49.94737 46.27778 ## MEM MIA MIL MIN NOR NYK OKL ## TotalPoints 490.05882 442.22222 466.41176 363.5 440.78947 421.2500 597.125 ## GamesPlayed 51.47059 42.88889 52.70588 38.4 47.15789 50.5625 57.875 ## ORL PHI PHO POR SAC SAN ## TotalPoints 523.13333 340.89474 513.7500 576.1875 487.05882 556.66667 ## GamesPlayed 56.13333 44.10526 51.0625 57.1875 51.29412 62.06667 ## TOR UTA WAS ## TotalPoints 568.33333 417.94118 476.05882 ## GamesPlayed 55.86667 46.17647 53.05882
想要同時針對隊伍和守備位置做分群呢?
用list將分群依據包起來
NBA1415TP<-split(NBA1415[,c("TotalPoints","GamesPlayed")],
list(NBA1415$Team,NBA1415$Position))
sapply(NBA1415TP, colMeans)#一樣可以用apply家族計算平均值
## ATL.C BOS.C BRO.C CHA.C CHI.C CLE.C DAL.C DEN.C DET.C ## TotalPoints NaN 744.5 687 618.66667 255 271.5 352.3333 489.0 608.5 ## GamesPlayed NaN 73.0 58 63.66667 45 33.5 53.0000 67.5 65.5 ## GSW.C HOU.C IND.C LAC.C LAL.C MEM.C MIA.C MIL.C MIN.C ## TotalPoints 214.3333 339.0 532.5 946 307 916 442 391.75 264.6 ## GamesPlayed 43.0000 26.5 68.5 82 67 81 54 60.00 30.4 ## NOR.C NYK.C OKL.C ORL.C PHI.C PHO.C POR.C SAC.C SAN.C ## TotalPoints 366.00000 388 850.0 822.0 346 432 425 520 NaN ## GamesPlayed 60.66667 51 72.5 66.5 48 69 59 36 NaN ## TOR.C UTA.C WAS.C ATL.PF BOS.PF BRO.PF CHA.PF ## TotalPoints 257.75 686 525.66667 592.00000 527 280.2 288.33333 ## GamesPlayed 32.50 82 63.33333 59.33333 59 41.6 54.66667 ## CHI.PF CLE.PF DAL.PF DEN.PF DET.PF GSW.PF HOU.PF IND.PF ## TotalPoints 732.75 964.5 624.0 473.66667 536.33333 589.5 613 476.0 ## GamesPlayed 60.00 79.0 60.5 52.33333 51.66667 62.5 64 57.5 ## LAC.PF LAL.PF MEM.PF MIA.PF MIL.PF MIN.PF NOR.PF NYK.PF ## TotalPoints 554.25 507.6667 310.4 368.25 263.00000 184.2 943 259 ## GamesPlayed 61.75 56.0000 35.8 36.75 34.33333 31.8 65 53 ## OKL.PF ORL.PF PHI.PF PHO.PF POR.PF SAC.PF SAN.PF ## TotalPoints 463.6667 320.00 265.5 669.75 842.5 298.16667 511.33333 ## GamesPlayed 54.0000 53.25 45.0 63.50 40.5 46.16667 67.16667 ## TOR.PF UTA.PF WAS.PF ATL.PG BOS.PG BRO.PG CHA.PG ## TotalPoints 541.5 442.50 507.6667 743.00000 718.00 641.33333 640.00 ## GamesPlayed 75.0 40.75 61.0000 68.33333 65.25 61.66667 52.75 ## CHI.PG CLE.PG DAL.PG DEN.PG DET.PG GSW.PG HOU.PG IND.PG ## TotalPoints 929.0 818.5 490.25 604.00000 498.00 1180.5 421.0 637.75 ## GamesPlayed 66.5 41.5 62.50 60.33333 43.25 79.0 61.5 56.00 ## LAC.PG LAL.PG MEM.PG MIA.PG MIL.PG MIN.PG NOR.PG NYK.PG ## TotalPoints 497.50 452.0 435.5 364.66667 371.2 417.25 240.33333 454.75 ## GamesPlayed 48.25 45.5 40.5 45.33333 45.0 45.50 33.66667 51.25 ## OKL.PG ORL.PG PHI.PG PHO.PG POR.PG SAC.PG SAN.PG TOR.PG ## TotalPoints 931.3333 459.5 272.00000 644.50 709 397.75 620.6667 1009.5 ## GamesPlayed 55.0000 64.5 38.33333 42.75 58 49.25 66.0000 76.0 ## UTA.PG WAS.PG ATL.SF BOS.SF BRO.SF CHA.SF CHI.SF ## TotalPoints 475.66667 603 506 256 590.66667 362.5 379.6667 ## GamesPlayed 39.66667 50 53 45 53.66667 42.0 57.0000 ## CLE.SF DAL.SF DEN.SF DET.SF GSW.SF HOU.SF IND.SF LAC.SF ## TotalPoints 592.75 492.75 909.5 317.75 874.0 549.5 353.5 498 ## GamesPlayed 59.00 60.75 68.5 55.50 80.5 61.5 51.5 69 ## LAL.SF MEM.SF MIA.SF MIL.SF MIN.SF NOR.SF NYK.SF OKL.SF ## TotalPoints 445.33333 1168 502.3333 681.5 389 209.5 443.2 354.00 ## GamesPlayed 42.33333 78 55.0000 52.0 50 41.0 52.0 46.25 ## ORL.SF PHI.SF PHO.SF POR.SF SAC.SF SAN.SF TOR.SF UTA.SF ## TotalPoints 661.0 517.75 479 374.66667 1012.5 547 408.5 428.4 ## GamesPlayed 56.5 59.00 59 57.66667 67.5 42 45.0 47.6 ## WAS.SF ATL.SG BOS.SG BRO.SG CHA.SG CHI.SG CLE.SG DAL.SG ## TotalPoints 401.0 428.5 442.0 516.5 475.00 609.00000 443.25 1513 ## GamesPlayed 51.8 56.5 56.5 58.5 58.25 62.33333 62.25 80 ## DEN.SG DET.SG GSW.SG HOU.SG IND.SG LAC.SG LAL.SG MEM.SG ## TotalPoints 216.0 853 514.1667 735.33333 942 371.000 420.25 407.4 ## GamesPlayed 39.4 71 54.5000 57.33333 70 36.125 30.25 58.8 ## MIA.SG MIL.SG MIN.SG NOR.SG NYK.SG OKL.SG ORL.SG ## TotalPoints 500.3333 784.66667 730.00000 722.6667 447.0 563.25 536.4 ## GamesPlayed 36.0000 74.66667 41.66667 51.0000 43.5 67.25 50.8 ## PHI.SG PHO.SG POR.SG SAC.SG SAN.SG TOR.SG UTA.SG WAS.SG ## TotalPoints 326.25 314.6 645.75 657.5 581.5 830.66667 270.00 393 ## GamesPlayed 30.25 41.0 62.75 77.5 61.5 55.33333 45.75 40
aggregate(資料, by=分組依據, FUN=函數功能)
aggregate(NBA1415$TotalPoints,
by=list(NBA1415$Team,NBA1415$Position),
FUN=mean)
## Group.1 Group.2 x ## 1 BOS C 744.5000 ## 2 BRO C 687.0000 ## 3 CHA C 618.6667 ## 4 CHI C 255.0000 ## 5 CLE C 271.5000 ## 6 DAL C 352.3333 ## 7 DEN C 489.0000 ## 8 DET C 608.5000 ## 9 GSW C 214.3333 ## 10 HOU C 339.0000 ## 11 IND C 532.5000 ## 12 LAC C 946.0000 ## 13 LAL C 307.0000 ## 14 MEM C 916.0000 ## 15 MIA C 442.0000 ## 16 MIL C 391.7500 ## 17 MIN C 264.6000 ## 18 NOR C 366.0000 ## 19 NYK C 388.0000 ## 20 OKL C 850.0000 ## 21 ORL C 822.0000 ## 22 PHI C 346.0000 ## 23 PHO C 432.0000 ## 24 POR C 425.0000 ## 25 SAC C 520.0000 ## 26 TOR C 257.7500 ## 27 UTA C 686.0000 ## 28 WAS C 525.6667 ## 29 ATL PF 592.0000 ## 30 BOS PF 527.0000 ## 31 BRO PF 280.2000 ## 32 CHA PF 288.3333 ## 33 CHI PF 732.7500 ## 34 CLE PF 964.5000 ## 35 DAL PF 624.0000 ## 36 DEN PF 473.6667 ## 37 DET PF 536.3333 ## 38 GSW PF 589.5000 ## 39 HOU PF 613.0000 ## 40 IND PF 476.0000 ## 41 LAC PF 554.2500 ## 42 LAL PF 507.6667 ## 43 MEM PF 310.4000 ## 44 MIA PF 368.2500 ## 45 MIL PF 263.0000 ## 46 MIN PF 184.2000 ## 47 NOR PF 943.0000 ## 48 NYK PF 259.0000 ## 49 OKL PF 463.6667 ## 50 ORL PF 320.0000 ## 51 PHI PF 265.5000 ## 52 PHO PF 669.7500 ## 53 POR PF 842.5000 ## 54 SAC PF 298.1667 ## 55 SAN PF 511.3333 ## 56 TOR PF 541.5000 ## 57 UTA PF 442.5000 ## 58 WAS PF 507.6667 ## 59 ATL PG 743.0000 ## 60 BOS PG 718.0000 ## 61 BRO PG 641.3333 ## 62 CHA PG 640.0000 ## 63 CHI PG 929.0000 ## 64 CLE PG 818.5000 ## 65 DAL PG 490.2500 ## 66 DEN PG 604.0000 ## 67 DET PG 498.0000 ## 68 GSW PG 1180.5000 ## 69 HOU PG 421.0000 ## 70 IND PG 637.7500 ## 71 LAC PG 497.5000 ## 72 LAL PG 452.0000 ## 73 MEM PG 435.5000 ## 74 MIA PG 364.6667 ## 75 MIL PG 371.2000 ## 76 MIN PG 417.2500 ## 77 NOR PG 240.3333 ## 78 NYK PG 454.7500 ## 79 OKL PG 931.3333 ## 80 ORL PG 459.5000 ## 81 PHI PG 272.0000 ## 82 PHO PG 644.5000 ## 83 POR PG 709.0000 ## 84 SAC PG 397.7500 ## 85 SAN PG 620.6667 ## 86 TOR PG 1009.5000 ## 87 UTA PG 475.6667 ## 88 WAS PG 603.0000 ## 89 ATL SF 506.0000 ## 90 BOS SF 256.0000 ## 91 BRO SF 590.6667 ## 92 CHA SF 362.5000 ## 93 CHI SF 379.6667 ## 94 CLE SF 592.7500 ## 95 DAL SF 492.7500 ## 96 DEN SF 909.5000 ## 97 DET SF 317.7500 ## 98 GSW SF 874.0000 ## 99 HOU SF 549.5000 ## 100 IND SF 353.5000 ## 101 LAC SF 498.0000 ## 102 LAL SF 445.3333 ## 103 MEM SF 1168.0000 ## 104 MIA SF 502.3333 ## 105 MIL SF 681.5000 ## 106 MIN SF 389.0000 ## 107 NOR SF 209.5000 ## 108 NYK SF 443.2000 ## 109 OKL SF 354.0000 ## 110 ORL SF 661.0000 ## 111 PHI SF 517.7500 ## 112 PHO SF 479.0000 ## 113 POR SF 374.6667 ## 114 SAC SF 1012.5000 ## 115 SAN SF 547.0000 ## 116 TOR SF 408.5000 ## 117 UTA SF 428.4000 ## 118 WAS SF 401.0000 ## 119 ATL SG 428.5000 ## 120 BOS SG 442.0000 ## 121 BRO SG 516.5000 ## 122 CHA SG 475.0000 ## 123 CHI SG 609.0000 ## 124 CLE SG 443.2500 ## 125 DAL SG 1513.0000 ## 126 DEN SG 216.0000 ## 127 DET SG 853.0000 ## 128 GSW SG 514.1667 ## 129 HOU SG 735.3333 ## 130 IND SG 942.0000 ## 131 LAC SG 371.0000 ## 132 LAL SG 420.2500 ## 133 MEM SG 407.4000 ## 134 MIA SG 500.3333 ## 135 MIL SG 784.6667 ## 136 MIN SG 730.0000 ## 137 NOR SG 722.6667 ## 138 NYK SG 447.0000 ## 139 OKL SG 563.2500 ## 140 ORL SG 536.4000 ## 141 PHI SG 326.2500 ## 142 PHO SG 314.6000 ## 143 POR SG 645.7500 ## 144 SAC SG 657.5000 ## 145 SAN SG 581.5000 ## 146 TOR SG 830.6667 ## 147 UTA SG 270.0000 ## 148 WAS SG 393.0000
aggregate(formula, data=資料, FUN=函數功能)
Formula: if you need all, put [.] into the formula (.~Team+Position)
aggregate(TotalPoints ~ Team+Position,
data = NBA1415, mean)
## Team Position TotalPoints ## 1 BOS C 744.5000 ## 2 BRO C 687.0000 ## 3 CHA C 618.6667 ## 4 CHI C 255.0000 ## 5 CLE C 271.5000 ## 6 DAL C 352.3333 ## 7 DEN C 489.0000 ## 8 DET C 608.5000 ## 9 GSW C 214.3333 ## 10 HOU C 339.0000 ## 11 IND C 532.5000 ## 12 LAC C 946.0000 ## 13 LAL C 307.0000 ## 14 MEM C 916.0000 ## 15 MIA C 442.0000 ## 16 MIL C 391.7500 ## 17 MIN C 264.6000 ## 18 NOR C 366.0000 ## 19 NYK C 388.0000 ## 20 OKL C 850.0000 ## 21 ORL C 822.0000 ## 22 PHI C 346.0000 ## 23 PHO C 432.0000 ## 24 POR C 425.0000 ## 25 SAC C 520.0000 ## 26 TOR C 257.7500 ## 27 UTA C 686.0000 ## 28 WAS C 525.6667 ## 29 ATL PF 592.0000 ## 30 BOS PF 527.0000 ## 31 BRO PF 280.2000 ## 32 CHA PF 288.3333 ## 33 CHI PF 732.7500 ## 34 CLE PF 964.5000 ## 35 DAL PF 624.0000 ## 36 DEN PF 473.6667 ## 37 DET PF 536.3333 ## 38 GSW PF 589.5000 ## 39 HOU PF 613.0000 ## 40 IND PF 476.0000 ## 41 LAC PF 554.2500 ## 42 LAL PF 507.6667 ## 43 MEM PF 310.4000 ## 44 MIA PF 368.2500 ## 45 MIL PF 263.0000 ## 46 MIN PF 184.2000 ## 47 NOR PF 943.0000 ## 48 NYK PF 259.0000 ## 49 OKL PF 463.6667 ## 50 ORL PF 320.0000 ## 51 PHI PF 265.5000 ## 52 PHO PF 669.7500 ## 53 POR PF 842.5000 ## 54 SAC PF 298.1667 ## 55 SAN PF 511.3333 ## 56 TOR PF 541.5000 ## 57 UTA PF 442.5000 ## 58 WAS PF 507.6667 ## 59 ATL PG 743.0000 ## 60 BOS PG 718.0000 ## 61 BRO PG 641.3333 ## 62 CHA PG 640.0000 ## 63 CHI PG 929.0000 ## 64 CLE PG 818.5000 ## 65 DAL PG 490.2500 ## 66 DEN PG 604.0000 ## 67 DET PG 498.0000 ## 68 GSW PG 1180.5000 ## 69 HOU PG 421.0000 ## 70 IND PG 637.7500 ## 71 LAC PG 497.5000 ## 72 LAL PG 452.0000 ## 73 MEM PG 435.5000 ## 74 MIA PG 364.6667 ## 75 MIL PG 371.2000 ## 76 MIN PG 417.2500 ## 77 NOR PG 240.3333 ## 78 NYK PG 454.7500 ## 79 OKL PG 931.3333 ## 80 ORL PG 459.5000 ## 81 PHI PG 272.0000 ## 82 PHO PG 644.5000 ## 83 POR PG 709.0000 ## 84 SAC PG 397.7500 ## 85 SAN PG 620.6667 ## 86 TOR PG 1009.5000 ## 87 UTA PG 475.6667 ## 88 WAS PG 603.0000 ## 89 ATL SF 506.0000 ## 90 BOS SF 256.0000 ## 91 BRO SF 590.6667 ## 92 CHA SF 362.5000 ## 93 CHI SF 379.6667 ## 94 CLE SF 592.7500 ## 95 DAL SF 492.7500 ## 96 DEN SF 909.5000 ## 97 DET SF 317.7500 ## 98 GSW SF 874.0000 ## 99 HOU SF 549.5000 ## 100 IND SF 353.5000 ## 101 LAC SF 498.0000 ## 102 LAL SF 445.3333 ## 103 MEM SF 1168.0000 ## 104 MIA SF 502.3333 ## 105 MIL SF 681.5000 ## 106 MIN SF 389.0000 ## 107 NOR SF 209.5000 ## 108 NYK SF 443.2000 ## 109 OKL SF 354.0000 ## 110 ORL SF 661.0000 ## 111 PHI SF 517.7500 ## 112 PHO SF 479.0000 ## 113 POR SF 374.6667 ## 114 SAC SF 1012.5000 ## 115 SAN SF 547.0000 ## 116 TOR SF 408.5000 ## 117 UTA SF 428.4000 ## 118 WAS SF 401.0000 ## 119 ATL SG 428.5000 ## 120 BOS SG 442.0000 ## 121 BRO SG 516.5000 ## 122 CHA SG 475.0000 ## 123 CHI SG 609.0000 ## 124 CLE SG 443.2500 ## 125 DAL SG 1513.0000 ## 126 DEN SG 216.0000 ## 127 DET SG 853.0000 ## 128 GSW SG 514.1667 ## 129 HOU SG 735.3333 ## 130 IND SG 942.0000 ## 131 LAC SG 371.0000 ## 132 LAL SG 420.2500 ## 133 MEM SG 407.4000 ## 134 MIA SG 500.3333 ## 135 MIL SG 784.6667 ## 136 MIN SG 730.0000 ## 137 NOR SG 722.6667 ## 138 NYK SG 447.0000 ## 139 OKL SG 563.2500 ## 140 ORL SG 536.4000 ## 141 PHI SG 326.2500 ## 142 PHO SG 314.6000 ## 143 POR SG 645.7500 ## 144 SAC SG 657.5000 ## 145 SAN SG 581.5000 ## 146 TOR SG 830.6667 ## 147 UTA SG 270.0000 ## 148 WAS SG 393.0000
計算平均的時候,該不該跳過缺值
x<-c(1,2,3,4,5,NA) mean(x)
## [1] NA
mean(x, na.rm=T)
## [1] 3
sum(x)
## [1] NA
sum(x, na.rm=T)
## [1] 15
Subset
x <- c(1, 2, NA, 4, NA, 5) x[! is.na(x)]
## [1] 1 2 4 5
complete.cases(x) #可使用在data frame,取出所有欄位都不是NA的row
## [1] TRUE TRUE FALSE TRUE FALSE TRUE
如果跳出是否要安裝Packages,選要
最後要放上GitHub,所以選From Template裡的GitHub Document
程式會產生內建的參考範例,先按“Knit”試試
分析NBA 2014到2015球季
所有作業除了有特別寫,遲交一天扣5分,最多扣40分(8天)
URL範例: https://github.com/yijutseng/BigDataCGUIM/blob/master/HW3.md
版本碼是指BigDataCGUIM的SHA碼
函數名稱<-function(參數1, 參數2, ….)({程式碼們}
四捨五入到小數點第二位
round2<-function(vector){
round(vector,digits = 2)
}
round2(3.886)
## [1] 3.89
沒用到Nothing參數,程式也不會出錯
round2Lazy<-function(vector,nothing){
round(vector,digits = 2)
}
round2(3.886)
## [1] 3.89
f <- function(a, b) {
print(a)
print(b)
}
f(45)
## [1] 45
## Error in print(b): argument "b" is missing, with no default
roundmean<-function(vector, ...){
round(mean(vector, ...),digits=2)
}
roundmean(c(1.1,2,3,4,5))
## [1] 3.02
roundmean(c(1.1,2,3,4,5,NA))
## [1] NA
roundmean(c(1.1,2,3,4,5,NA),na.rm=T)
## [1] 3.02
roundmean<-function(vector, ...){
round(mean(vector, ...),digits=2)
}
roundmean(c(1.1,2,3,4,5))
## [1] 3.02
roundmean(c(1.1,2,3,4,5,NA))
## [1] NA
roundmean(c(1.1,2,3,4,5,NA),na.rm=T)
## [1] 3.02
使用者如果沒有指定參數值,直接帶入預設
roundDe<-function(v=1.111:10.111){
round(v,digits = 2)
}
roundDe(1.66:6.66)
## [1] 1.66 2.66 3.66 4.66 5.66 6.66
roundDe()
## [1] 1.11 2.11 3.11 4.11 5.11 6.11 7.11 8.11 9.11 10.11
停止函數執行,並回傳值
round2<-function(v){
if(!is.numeric(v)){
print("輸入數字")
return()
}
round(v,digits = 2)
}
round2("a")
## [1] "輸入數字"
## NULL
apply(Data, MARGIN, FUN,…)
apply(iris,2,max)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## "7.9" "4.4" "6.9" "2.5" "virginica"
RoundNumber2<-function(v,XFun){
round(XFun(v),digits = 2)
}
RoundNumber2(1.1:10.1,mean)
## [1] 5.6
http://en.wikipedia.org/wiki/Data
Data are values of qualitative or quantitative variables, belonging to a set of items.
學會:
Raw data -> Processing script -> Tidy data -> Data analysis -> Data communication
NBA1415
## League Name Team Position GamesPlayed TotalMinutesPlayed ## 1 NBA Quincy Acy NYK SF 68 1288 ## 2 NBA Jordan Adams MEM SG 30 249 ## 3 NBA Steven Adams OKL C 70 1776 ## 4 NBA Jeff Adrien MIN PF 17 215 ## 5 NBA Arron Afflalo POR SG 78 2502 ## 6 NBA Alexis Ajinca NOR C 68 956 ## 7 NBA Furkan Aldemir PHI PF 41 541 ## 8 NBA Cole Aldrich NYK C 61 979 ## 9 NBA Lamarcu Aldridge POR PF 71 2514 ## 10 NBA Lavoy Allen IND PF 63 1066 ## 11 NBA Tony Allen MEM SG 63 1647 ## 12 NBA Al-farouq Aminu DAL SF 74 1368 ## 13 NBA Lou Amundson NYK PF 53 937 ## 14 NBA Chris Andersen MIA C 60 1131 ## 15 NBA Alan Anderson BRO SG 74 1743 ## 16 NBA Kyle Anderson SAN SG 33 355 ## 17 NBA Ryan Anderson NOR PF 61 1677 ## 18 NBA G Antetokounmpo MIL SG 81 2542 ## 19 NBA Carmelo Anthony NYK SF 40 1430 ## 20 NBA Joel Anthony DET C 49 408 ## 21 NBA Pero Antic ATL PF 63 1041 ## 22 NBA Trevor Ariza HOU SG 82 2936 ## 23 NBA Darrell Arthur DEN PF 58 986 ## 24 NBA Omer Asik NOR C 77 1979 ## 25 NBA D.j. Augustin OKL PG 82 1964 ## 26 NBA Jeff Ayres SAN PF 51 386 ## 27 NBA Luke Babbitt NOR SF 63 828 ## 28 NBA Cameron Bairstow CHI PF 18 67 ## 29 NBA Leandro Barbosa GSW SG 66 983 ## 30 NBA J.j. Barea DAL PG 77 1368 ## 31 NBA Andrea Bargnani NYK C 29 785 ## 32 NBA Harrison Barnes GSW SF 82 2317 ## 33 NBA Matt Barnes LAC SF 76 2276 ## 34 NBA Earl Barron PHO PF 16 143 ## 35 NBA Will Barton DEN SG 58 982 ## 36 NBA Brandon Bass BOS PF 82 1932 ## 37 NBA Nicolas Batum POR SF 71 2380 ## 38 NBA Jerryd Bayless MIL PG 77 1723 ## 39 NBA Aron Baynes SAN PF 70 1126 ## 40 NBA Kent Bazemore ATL SG 75 1327 ## 41 NBA Bradley Beal WAS SG 63 2109 ## 42 NBA Michael Beasley MIA PF 24 503 ## 43 NBA Marco Belinelli SAN SG 62 1386 ## 44 NBA Jerrelle Benimon UTA PF 2 3 ## 45 NBA Anthony Bennett MIN PF 57 898 ## 46 NBA Patrick Beverley HOU PG 56 1729 ## 47 NBA Sim Bhullar SAC C 3 3 ## 48 NBA Bismack Biyombo CHA C 64 1240 ## 49 NBA Tarik Black LAL PF 63 1195 ## 50 NBA Dejuan Blair WAS C 29 179 ## 51 NBA Steve Blake POR PG 81 1529 ## 52 NBA Eric Bledsoe PHO PG 81 2799 ## 53 NBA Vander Blue LAL SG 2 74 ## 54 NBA Bojan Bogdanovic BRO SF 78 1856 ## 55 NBA Andrew Bogut GSW C 67 1583 ## 56 NBA Matt Bonner SAN PF 72 938 ## 57 NBA Trevor Booker UTA PF 79 1562 ## 58 NBA Carlos Boozer LAL PF 71 1692 ## 59 NBA Chris Bosh MIA PF 44 1557 ## 60 NBA Avery Bradley BOS PG 77 2427 ## 61 NBA Elton Brand ATL PF 36 487 ## 62 NBA Corey Brewer HOU SF 80 2088 ## 63 NBA Aaron Brooks CHI PG 82 1885 ## 64 NBA Jabari Brown LAL SG 19 567 ## 65 NBA Lorenzo Brown MIN PG 29 550 ## 66 NBA Markel Brown BRO SG 47 784 ## 67 NBA Shannon Brown MIA PG 5 89 ## 68 NBA Kobe Bryant LAL SG 35 1206 ## 69 NBA Chase Budinger MIN SF 67 1287 ## 70 NBA Reggie Bullock PHO SG 36 339 ## 71 NBA Trey Burke UTA PG 76 2286 ## 72 NBA Alec Burks UTA PG 27 899 ## 73 NBA Caron Butler DET SF 78 1626 ## 74 NBA Jimmy Butler CHI SG 65 2515 ## 75 NBA Rasual Butler WAS SF 75 1509 ## 76 NBA Dwight Buycks LAL PG 6 124 ## 77 NBA Will Bynum WAS PG 7 67 ## 78 NBA Bruno Caboclo TOR SF 8 24 ## 79 NBA Nick Calathes MEM SG 58 835 ## 80 NBA Jose Calderon NYK PG 42 1269 ## 81 NBA Ke Caldwell-pope DET SG 82 2591 ## 82 NBA Isaiah Canaan PHI PG 48 940 ## 83 NBA Clint Capela HOU C 12 90 ## 84 NBA Demarre Carroll ATL SF 71 2188 ## 85 NBA Vince Carter MEM SG 66 1092 ## 86 NBA Carter-williams MIL PG 67 2151 ## 87 NBA Omri Casspi SAC SF 67 1418 ## 88 NBA Mario Chalmers MIA PG 80 2368 ## 89 NBA Tyson Chandler DAL C 75 2286 ## 90 NBA Wilson Chandler DEN SF 78 2474 ## 91 NBA Will Cherry CLE PG 8 70 ## 92 NBA Patr Christopher UTA SG 4 29 ## 93 NBA Earl Clark BRO PF 9 83 ## 94 NBA Ian Clark DEN SG 30 193 ## 95 NBA Jordan Clarkson LAL PG 59 1474 ## 96 NBA Victor Claver POR PF 10 75 ## 97 NBA Norris Cole NOR PG 75 1830 ## 98 NBA Darren Collison SAC PG 45 1562 ## 99 NBA Nick Collison OKL PF 66 1105 ## 100 NBA Mike Conley MEM PG 70 2225 ## 101 NBA Jack Cooley UTA SF 16 86 ## 102 NBA Chris Copeland IND SF 50 829 ## 103 NBA Bryce Cotton UTA PG 16 162 ## 104 NBA Demarcus Cousins SAC C 59 2013 ## 105 NBA Robert Covington PHI SF 69 1930 ## 106 NBA Allen Crabbe POR SG 51 684 ## 107 NBA Jamal Crawford LAC SG 64 1706 ## 108 NBA Jae Crowder BOS SF 82 1654 ## 109 NBA Dante Cunningham NOR PF 66 1650 ## 110 NBA Jared Cunningham LAC SG 19 91 ## 111 NBA Seth Curry PHO PG 2 8 ## 112 NBA Stephen Curry GSW PG 80 2613 ## 113 NBA Samuel Dalembert NYK C 32 544 ## 114 NBA Troy Daniels CHA SG 47 401 ## 115 NBA Luigi Datome BOS SF 21 211 ## 116 NBA Brandon Davies BRO PF 27 424 ## 117 NBA Anthony Davis NOR PF 68 2460 ## 118 NBA Ed Davis LAL PF 79 1842 ## 119 NBA Glen Davis LAC PF 74 909 ## 120 NBA Andre Dawkins MIA SG 4 22 ## 121 NBA Austin Daye ATL SF 35 348 ## 122 NBA Dewayne Dedmon ORL C 59 849 ## 123 NBA Matt Dellavedova CLE SG 67 1378 ## 124 NBA Luol Deng MIA SF 72 2420 ## 125 NBA Demar Derozan TOR SG 60 2102 ## 126 NBA Boris Diaw SAN PF 81 1987 ## 127 NBA Gorgui Dieng MIN C 73 2193 ## 128 NBA Spence Dinwiddie DET PG 34 458 ## 129 NBA Joey Dorsey HOU PF 69 856 ## 130 NBA Toney Douglas NOR PG 12 177 ## 131 NBA Douglas-roberts LAC SG 12 103 ## 132 NBA Goran Dragic MIA SG 78 2641 ## 133 NBA Zoran Dragic MIA SG 16 76 ## 134 NBA Larry Drew PHI PG 12 220 ## 135 NBA Andre Drummond DET C 82 2497 ## 136 NBA Jared Dudley MIL SG 72 1716 ## 137 NBA Tim Duncan SAN PF 77 2224 ## 138 NBA Mike Dunleavy CHI SF 63 1837 ## 139 NBA Kevin Durant OKL SF 27 914 ## 140 NBA Cleanthony Early NYK SF 39 651 ## 141 NBA Wayne Ellington LAL SG 65 1676 ## 142 NBA Monta Ellis DAL SG 80 2698 ## 143 NBA James Ennis MIA SF 62 1052 ## 144 NBA Tyler Ennis MIL PG 33 413 ## 145 NBA Jeremy Evans UTA SF 38 266 ## 146 NBA Reggie Evans SAC PF 47 767 ## 147 NBA Tyreke Evans NOR SG 79 2695 ## 148 NBA Dante Exum UTA SG 82 1821 ## 149 NBA Festus Ezeli GSW C 46 504 ## 150 NBA Kenneth Faried DEN PF 75 2085 ## 151 NBA Jordan Farmar LAC PG 36 528 ## 152 NBA Derrick Favors UTA PF 74 2281 ## 153 NBA Raymond Felton DAL PG 29 281 ## 154 NBA Landry Fields TOR SG 26 217 ## 155 NBA Evan Fournier ORL SG 58 1659 ## 156 NBA Randy Foye DEN SG 51 1089 ## 157 NBA Jamaal Franklin DEN SG 3 14 ## 158 NBA Tim Frazier POR PG 11 241 ## 159 NBA Jimmer Fredette NOR PG 50 514 ## 160 NBA Joel Freeland POR C 48 618 ## 161 NBA Channing Frye ORL PF 75 1867 ## 162 NBA Danilo Gallinari DEN SF 59 1421 ## 163 NBA Langsto Galloway NYK PG 45 1459 ## 164 NBA Francisco Garcia HOU SG 14 201 ## 165 NBA Kevin Garnett MIN PF 47 948 ## 166 NBA Marc Gasol MEM C 81 2690 ## 167 NBA Pau Gasol CHI PF 78 2682 ## 168 NBA Rudy Gay SAC SF 68 2412 ## 169 NBA Alonzo Gee POR SF 54 664 ## 170 NBA Paul George IND SF 6 92 ## 171 NBA Taj Gibson CHI PF 62 1694 ## 172 NBA Manu Ginobili SAN SG 70 1591 ## 173 NBA Rudy Gobert UTA C 82 2163 ## 174 NBA Drew Gooden WAS PF 51 866 ## 175 NBA Archie Goodwin PHO SG 41 535 ## 176 NBA Aaron Gordon ORL PF 47 797 ## 177 NBA Ben Gordon ORL SG 56 789 ## 178 NBA Drew Gordon PHI PF 9 72 ## 179 NBA Eric Gordon NOR SG 61 2020 ## 180 NBA Marcin Gortat WAS C 82 2455 ## 181 NBA Danny Granger MIA SF 31 611 ## 182 NBA Jerami Grant PHI SF 65 1380 ## 183 NBA Danny Green SAN SG 81 2311 ## 184 NBA Draymond Green GSW SF 79 2487 ## 185 NBA Erick Green DEN PG 43 414 ## 186 NBA Gerald Green PHO SG 74 1447 ## 187 NBA Jamychal Green MEM PF 24 165 ## 188 NBA Jeff Green MEM SF 78 2459 ## 189 NBA Willie Green ORL SG 52 953 ## 190 NBA Blake Griffin LAC PF 67 2354 ## 191 NBA Jorge Gutierrez MIL PG 20 176 ## 192 NBA P.j. Hairston CHA SG 45 690 ## 193 NBA Jordan Hamilton LAC SG 14 123 ## 194 NBA Justin Hamilton MIN C 41 709 ## 195 NBA Tyler Hansbrough TOR PF 74 1057 ## 196 NBA Tim Hardaway NYK SG 70 1683 ## 197 NBA James Harden HOU SG 81 2979 ## 198 NBA Maurice Harkless ORL SF 45 674 ## 199 NBA Devin Harris DAL PG 76 1686 ## 200 NBA Gary Harris DEN SG 55 724 ## 201 NBA Joe Harris CLE SG 50 491 ## 202 NBA Tobias Harris ORL SF 68 2367 ## 203 NBA Udonis Haslem MIA PF 62 1000 ## 204 NBA Spencer Hawes LAC PF 73 1276 ## 205 NBA Chuck Hayes TOR C 29 255 ## 206 NBA Gordon Hayward UTA SF 76 2618 ## 207 NBA Brendan Haywood CLE C 22 120 ## 208 NBA Gerald Henderson CHA SG 80 2323 ## 209 NBA Xavier Henry LAL SF 9 83 ## 210 NBA John Henson MIL C 67 1233 ## 211 NBA Roy Hibbert IND C 76 1930 ## 212 NBA J.j. Hickson DEN C 73 1415 ## 213 NBA Nene Hilario WAS PF 68 1696 ## 214 NBA George Hill IND PG 43 1266 ## 215 NBA Jordan Hill LAL PF 70 1877 ## 216 NBA Solomon Hill IND SF 82 2380 ## 217 NBA Kirk Hinrich CHI SG 66 1611 ## 218 NBA Jrue Holiday NOR PG 40 1301 ## 219 NBA Justin Holiday GSW SG 59 658 ## 220 NBA Ryan Hollins SAC C 46 444 ## 221 NBA Rodney Hood UTA SG 50 1070 ## 222 NBA Al Horford ATL PF 76 2318 ## 223 NBA Dwight Howard HOU C 41 1224 ## 224 NBA Lester Hudson LAC SG 5 56 ## 225 NBA Robbie Hummel MIN SF 45 744 ## 226 NBA Kris Humphries WAS PF 64 1346 ## 227 NBA Serge Ibaka OKL PF 64 2119 ## 228 NBA Andre Iguodala GSW SG 77 2066 ## 229 NBA Ersan Ilyasova MIL PF 58 1319 ## 230 NBA Joe Ingles UTA SF 79 1673 ## 231 NBA Kyrie Irving CLE PG 75 2735 ## 232 NBA Jarrett Jack BRO PG 80 2243 ## 233 NBA Reggie Jackson DET PG 77 2269 ## 234 NBA Bernard James DAL C 16 156 ## 235 NBA Lebron James CLE SF 69 2497 ## 236 NBA Al Jefferson CHA C 65 1991 ## 237 NBA Cory Jefferson BRO PF 50 535 ## 238 NBA Richar Jefferson DAL SF 74 1248 ## 239 NBA John Jenkins ATL SG 24 297 ## 240 NBA Brandon Jennings DET PG 41 1172 ## 241 NBA Jonas Jerebko BOS PF 75 1231 ## 242 NBA Grant Jerrett UTA PF 8 53 ## 243 NBA Amir Johnson TOR PF 75 1979 ## 244 NBA Chris Johnson UTA SF 29 526 ## 245 NBA James Johnson TOR PF 70 1368 ## 246 NBA Joe Johnson BRO SG 80 2787 ## 247 NBA Nick Johnson HOU SG 28 262 ## 248 NBA Tyler Johnson MIA SG 32 600 ## 249 NBA Wesley Johnson LAL SF 76 2244 ## 250 NBA Dahntay Jones LAC PG 33 130 ## 251 NBA James Jones CLE SF 57 652 ## 252 NBA Perry Jones OKL SF 43 633 ## 253 NBA Terrence Jones HOU PF 33 887 ## 254 NBA Deandre Jordan LAC C 82 2819 ## 255 NBA Jerome Jordan BRO C 44 381 ## 256 NBA Cory Joseph SAN PG 79 1445 ## 257 NBA Chris Kaman POR C 74 1400 ## 258 NBA Enes Kanter OKL C 75 2139 ## 259 NBA Sergey Karasev BRO SG 33 558 ## 260 NBA Ryan Kelly LAL PF 52 1232 ## 261 NBA M Kidd-gilchrist CHA SF 55 1588 ## 262 NBA Sean Kilpatrick MIN SG 4 71 ## 263 NBA Andrei Kirilenko BRO SF 7 37 ## 264 NBA Alex Kirk CLE C 5 15 ## 265 NBA Brandon Knight PHO PG 63 2040 ## 266 NBA Kyle Korver ATL SG 75 2418 ## 267 NBA Kosta Koufos MEM C 81 1350 ## 268 NBA Ognjen Kuzmic GSW C 16 72 ## 269 NBA Jeremy Lamb OKL SG 47 635 ## 270 NBA Carl Landry SAC PF 70 1195 ## 271 NBA Shane Larkin NYK PG 76 1864 ## 272 NBA Joffre Lauvergne DEN PF 24 267 ## 273 NBA Zach Lavine MIN PG 77 1906 ## 274 NBA Ty Lawson DEN PG 75 2668 ## 275 NBA Ricky Ledo NYK SG 17 245 ## 276 NBA Courtney Lee MEM SG 77 2355 ## 277 NBA David Lee GSW PF 49 904 ## 278 NBA Malcolm Lee PHI SG 1 2 ## 279 NBA Alex Len PHO C 69 1518 ## 280 NBA Kawhi Leonard SAN SF 64 2031 ## 281 NBA Meyers Leonard POR C 55 850 ## 282 NBA Jon Leuer MEM PF 63 826 ## 283 NBA Damian Lillard POR PG 82 2928 ## 284 NBA Jeremy Lin LAL PG 74 1910 ## 285 NBA Shaun Livingston GSW PG 78 1469 ## 286 NBA Brook Lopez BRO C 72 2104 ## 287 NBA Robin Lopez POR C 59 1638 ## 288 NBA Kevin Love CLE PF 76 2562 ## 289 NBA Kyle Lowry TOR PG 70 2422 ## 290 NBA John Lucas DET PG 21 273 ## 291 NBA Kalin Lucas MEM PG 1 6 ## 292 NBA Shelvin Mack ATL PG 55 832 ## 293 NBA Ian Mahinmi IND C 61 1146 ## 294 NBA Devyn Marble ORL SG 16 212 ## 295 NBA Shawn Marion CLE SF 58 1108 ## 296 NBA Kendall Marshall MIL PG 28 416 ## 297 NBA Cartier Martin DET SF 23 200 ## 298 NBA Kenyon Martin MIL PF 11 106 ## 299 NBA Kevin Martin MIN SG 39 1303 ## 300 NBA Wesley Matthews POR SG 60 2027 ## 301 NBA Jason Maxiell CHA PF 61 880 ## 302 NBA O.j. Mayo MIL SG 71 1696 ## 303 NBA Luc Mbah_a_moute PHI SF 67 1916 ## 304 NBA James Mcadoo GSW SG 15 140 ## 305 NBA Ray Mccallum SAC PG 68 1434 ## 306 NBA C.j. Mccollum POR SG 62 973 ## 307 NBA K.j. Mcdaniels HOU SG 62 1353 ## 308 NBA Doug Mcdermott CHI SF 36 320 ## 309 NBA Mitch Mcgary OKL PF 32 485 ## 310 NBA Javale Mcgee PHI C 23 256 ## 311 NBA Ben Mclemore SAC SG 82 2674 ## 312 NBA Jerel Mcneal PHO SG 6 36 ## 313 NBA Josh Mcroberts MIA PF 17 297 ## 314 NBA Jodie Meeks DET SG 60 1463 ## 315 NBA Gal Mekel NOR PG 4 44 ## 316 NBA Khris Middleton MIL SF 79 2378 ## 317 NBA C.j. Miles IND SG 70 1843 ## 318 NBA Andre Miller SAC PG 81 1252 ## 319 NBA Darius Miller NOR SF 5 44 ## 320 NBA Mike Miller CLE SF 52 698 ## 321 NBA Quincy Miller DET PF 10 120 ## 322 NBA Patty Mills SAN PG 51 799 ## 323 NBA Elijah Millsap UTA SG 47 924 ## 324 NBA Paul Millsap ATL PF 73 2390 ## 325 NBA Nikola Mirotic CHI PF 82 1658 ## 326 NBA Nazr Mohammed CHI C 23 131 ## 327 NBA Greg Monroe DET PF 69 2138 ## 328 NBA E'twaun Moore CHI SG 56 507 ## 329 NBA Eric Moreland SAC PF 3 3 ## 330 NBA Darius Morris BRO PG 37 295 ## 331 NBA Marcus Morris PHO PF 81 2046 ## 332 NBA Markieff Morris PHO PF 82 2581 ## 333 NBA Anthony Morrow OKL SG 74 1808 ## 334 NBA Donat Motiejunas HOU PF 71 2040 ## 335 NBA Timofey Mozgov CLE C 81 2058 ## 336 NBA Shabazz Muhammad MIN SF 38 867 ## 337 NBA Toure' Murry WAS SF 5 19 ## 338 NBA Mike Muscala ATL PF 40 503 ## 339 NBA Shabazz Napier MIA PG 51 1014 ## 340 NBA Gary Neal MIN PG 54 1193 ## 341 NBA Jameer Nelson DEN PG 63 1403 ## 342 NBA Andrew Nicholson ORL PF 40 491 ## 343 NBA Joakim Noah CHI C 67 2047 ## 344 NBA Nerlens Noel PHI PF 75 2311 ## 345 NBA Lucas Nogueira TOR C 4 13 ## 346 NBA Steve Novak OKL SF 35 204 ## 347 NBA Dirk Nowitzki DAL PF 77 2285 ## 348 NBA Jusuf Nurkic DEN C 62 1105 ## 349 NBA Johnny O'bryant MIL PF 34 365 ## 350 NBA Kyle O'quinn ORL PF 51 825 ## 351 NBA Victor Oladipo ORL SG 72 2572 ## 352 NBA Kelly Olynyk BOS C 64 1424 ## 353 NBA Arinze Onuaku MIN PF 6 70 ## 354 NBA Zaza Pachulia MIL C 73 1730 ## 355 NBA Kos Papanikolaou HOU SF 43 796 ## 356 NBA Jannero Pargo CHA PG 9 74 ## 357 NBA Jabari Parker MIL SF 25 738 ## 358 NBA Tony Parker SAN PG 68 1957 ## 359 NBA Chandler Parsons DAL SF 66 2186 ## 360 NBA Patric Patterson TOR PF 81 2160 ## 361 NBA Chris Paul LAC PG 82 2860 ## 362 NBA Adreian Payne MIN PF 32 741 ## 363 NBA Elfrid Payton ORL PG 82 2490 ## 364 NBA Nikola Pekovic MIN C 31 817 ## 365 NBA Kendrick Perkins DAL C 68 1147 ## 366 NBA Paul Pierce WAS SF 73 1913 ## 367 NBA Mason Plumlee BRO PF 82 1739 ## 368 NBA Miles Plumlee MIL C 73 1195 ## 369 NBA Quincy Pondexter NOR SF 75 1792 ## 370 NBA Otto Porter WAS SF 74 1438 ## 371 NBA Dwight Powell DAL SF 29 237 ## 372 NBA Phil Pressey BOS PG 50 601 ## 373 NBA A.j. 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讀檔的基本函數們:
read.table, read.csv, for reading tabular datareadLines, for reading lines of a text filesource, for reading in R code files (inverse of dump)dget, for reading in R code files (inverse of dput)load, for reading in saved workspacesThere are analogous functions for writing data to files
The read.table function is one of the most commonly used functions for reading data. It has a few important arguments: - file, the name of a file, or a connection - header, logical indicating if the file has a header line - sep, a string indicating how the columns are separated - colClasses, a character vector indicating the class of each column in the dataset - nrows, the number of rows in the dataset - comment.char, a character string indicating the comment character - skip, the number of lines to skip from the beginning - stringsAsFactors, should character variables be coded as factors?
For small to moderately sized datasets, you can usually call read.table without specifying any other arguments
data <- read.table("foo.txt")
R will automatically
read.csv is identical to read.table except that the default separator is a comma.With much larger datasets, doing the following things will make your life easier and will prevent R from choking.
comment.char = "" if there are no commented lines in your file.colClasses argument. Specifying this option instead of using the default can make ’read.table’ run MUCH faster, often twice as fast. In order to use this option, you have to know the class of each column in your data frame. If all of the columns are “numeric”, for example, then you can just set colClasses = "numeric". A quick an dirty way to figure out the classes of each column is the following:initial <- read.table("datatable.txt", nrows = 100)
classes <- sapply(initial, class)
tabAll <- read.table("datatable.txt",
colClasses = classes)
nrows. This doesn’t make R run faster but it helps with memory usage. A mild overestimate is okay. You can use the Unix tool wc to calculate the number of lines in a file.In general, when using R with larger datasets, it’s useful to know a few things about your system.
I have a data frame with 1,500,000 rows and 120 columns, all of which are numeric data. Roughly, how much memory is required to store this data frame?
1,500,000 × 120 × 8 bytes/numeric
= 1440000000 bytes
= 1440000000 / \(2^{20}\) bytes/MB
= 1,373.29 MB
= 1.34 GB
dumping and dputing are useful because the resulting textual format is edit-able, and in the case of corruption, potentially recoverable.Unlike writing out a table or csv file, dump and dput preserve the metadata (sacrificing some readability), so that another user doesn’t have to specify it all over again.Textual formats can work much better with version control programs like subversion or git which can only track changes meaningfully in text filesAnother way to pass data around is by deparsing the R object with dput and reading it back in using dget.
> y <- data.frame(a = 1, b = "a")
> dput(y)
structure(list(a = 1,
b = structure(1L, .Label = "a",
class = "factor")),
.Names = c("a", "b"), row.names = c(NA, -1L),
class = "data.frame")
> dput(y, file = "y.R")
> new.y <- dget("y.R")
> new.y
a b
1 1 a
Multiple objects can be deparsed using the dump function and read back in using source.
> x <- "foo"
> y <- data.frame(a = 1, b = "a")
> dump(c("x", "y"), file = "data.R")
> rm(x, y)
> source("data.R")
> y
a b
1 1 a
> x
[1] "foo"
Data are read in using connection interfaces. Connections can be made to files (most common) or to other more exotic things.
file, opens a connection to a filegzfile, opens a connection to a file compressed with gzipbzfile, opens a connection to a file compressed with bzip2url, opens a connection to a webpage> str(file)
function (description = "", open = "", blocking = TRUE,
encoding = getOption("encoding"))
description is the name of the fileopen is a code indicatingIn general, connections are powerful tools that let you navigate files or other external objects. In practice, we often don’t need to deal with the connection interface directly.
con <- file("foo.txt", "r")
data <- read.csv(con)
close(con)
is the same as
data <- read.csv("foo.txt")
> con <- gzfile("words.gz")
> x <- readLines(con, 10)
> x
[1] "1080" "10-point" "10th" "11-point"
[5] "12-point" "16-point" "18-point" "1st"
[9] "2" "20-point"
writeLines takes a character vector and writes each element one line at a time to a text file.
readLines can be useful for reading in lines of webpages
## This might take time
con <- url("http://www.jhsph.edu", "r")
x <- readLines(con)
> head(x)
[1] "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0 Transitional//EN\">"
[2] ""
[3] "<html>"
[4] "<head>"
[5] "\t<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8